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Organic field effect transistors (OFETs), used in the fabrication of nano‐sensors, are one of the most promising devices in organic electronics because of their lightweight, flexible, and low fabrication cost. However, the numerical modeling of such OFETs is still in an early stage due to the minimal analytical as well as numerical models presented in the literature. This research aims to demonstrate an experimentally verified machine‐learning model by investigating an OFET with polyaniline as a p‐type organic semiconductor. OFET's threshold voltage, on/off current ratio, subthreshold swing, and device mobilities are studied as the primary output chiasmatic parameters. The random‐forest machine learning model has shown the criticality of the doping effect on turning the OFET to depletion mode, with positive threshold voltage, under doping higher than 5×1014$$ 5\times {10}^{14} $$ cm−3. Additionally, the study highlights the effectiveness of the gate oxide thickness in controlling the OFET threshold voltage. A 50 nm oxide thickness showed sufficiency to have a non‐depleted OFET operation. [ABSTRACT FROM AUTHOR]

Thin film transistors (TFTs) are ranked as one of the promising field‐effect transistors in the electronic industry. TFTs showed a great potential in many applications where liquid crystal displays, touchscreens, and biosensors are of the leading. In the current study, we are demonstrating a numerical carrier transport model based on finite element method (FEM), to investigate InGaZnO (IGZO) based TFTs. Amorphous silicon TFT has been simulated as a bare device. The impact of scaling down the dimension of the TFT, up to 30 nm channel length, on the output performance characteristics of the transistor has been addressed. Additionally, the effects of active semiconductor layer doping concentration and oxide layer thickness have been investigated. Moreover, a novel approach is introduced to correlate the selected input design parameters with a set of characteristic outputs including threshold voltage, on/off current ratio, and subthreshold swing. The proposed model correlates input and outputs using a characteristic matrix with nine coefficients, making it possible to predict, that is, interpolate, the characteristic outputs within the system boundaries. The suggested model was verified with respect to input data as well as data from the literature showing a very acceptable agreement. [ABSTRACT FROM AUTHOR]